geochemical mapping of new mexico, usa, using stream sediment data
TRANSCRIPT
ORIGINAL ARTICLE
Geochemical mapping of New Mexico, USA, using streamsediment data
Taisser Zumlot Æ Philip Goodell Æ Fares Howari
Received: 23 September 2008 / Accepted: 14 November 2008 / Published online: 6 January 2009
� Springer-Verlag 2008
Abstract The spatial analysis of geochemical data has
several environmental and geological applications. The
present study investigated the regional distribution of Al,
Ba, Ca, Ce, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, Pb,
Sc, Th, Ti, U, V, and Zn elements in stream sediment
samples from New Mexico State. These elements were
studied in order to integrate them with geological and
environmental characteristics of the area. Data are used
from 27,798 samples that were originally collected during
the national uranium resource evaluation (NURE)
Hydrogeochemical and stream sediment reconnaissance
(HSSR) program in the 1970s. The original data are
available as U.S. Geological Survey Open-File Report
97-492. The study used a variety of data processing and
filtering techniques that included univariate, bivariate,
factor analyses and spatial analyses to transform the data
into a useable format. Principal component analysis and
GIS techniques are applied to classify the elements and to
identify geochemical signatures, either natural or anthro-
pogenic. The study found that the distribution of the
investigated elements is mainly controlled by the bed rock
chemistry. For example, along the Rio Grande rift and
Jemez lineament a strong association between Co, Cr, Cu,
Fe, Ni, Sc, Ti, V and Zn was observed and indicates that
elements distribution in the area controlled by the mafic
factor. The rare earth elements (REE) factor which is
consists of Ce, La and U, also has strong, localized, clusters
in the felsic centers in New Mexico.
Keywords Geochemical mapping � Stream sediments �Principal component analysis � GIS � New Mexico, USA
Introduction
Regional geochemical mapping is the study of the chem-
istry of the surface of the earth and is fundamental
scientific knowledge. It can be considered as an important
tool in environmental studies, geologic mapping and min-
eral exploration. Objectives of regional geochemical
mapping include: (1) elucidation of fundamental earth
systems processes, (2) exploration for new mineral
resources using geochemical anomalies, (3) determination
of geochemical background values, and (4) identification of
natural or man made chemical contamination. The United
Nations and the International Union of Geological Sciences
are working toward a systematic global geochemical
database and a world geochemical atlas (IUGS 2006;
Darnely 1995). Table 1 gives a summary of several
regional geochemical mapping studies. The Wolfson
Geochemical Atlas of England and Wales developed in the
1960s by J. S. Webb at the Applied Geochemistry Research
Center in England, consisted of over 50,000 stream sedi-
ment samples that were obtained over an area of about
66,800 square miles (Howarth and Thornton 1983). These
T. Zumlot
Center for Environmental Resource Management (CERM),
University of Texas at El Paso, El Paso, TX 79968, USA
P. Goodell
Department of Geological Sciences,
University of Texas at El Paso,
El Paso, TX 79968, USA
F. Howari (&)
Bureau of Economic Geology,
Jackson School of Geosciences,
The University of Texas at Austin,
Austin, TX 78758, USA
e-mail: [email protected]
123
Environ Geol (2009) 58:1479–1497
DOI 10.1007/s00254-008-1650-0
samples were analyzed for 35 different chemical elements.
The survey helped to understand certain environmental
issues such as diseases like molibdicosis (Thornton 1983)
in cattle, and others. Salminen and Tarvainen (1995) have
produced regional geochemical maps of Finland. The entire
country was covered at a reconnaissance scale using glacial
till, ground water, surface water and stream sediment as
sampling media. The study indicated the presence of gold
and multiple-sulphide ore deposits. Many other countries
have followed this trend, and Mexico has recently com-
pleted a countrywide survey.
In the USA the national uranium resource evaluation
(NURE) program sampled over 250,000 sites in the conti-
nental US and analyzed them for up to 40 constituents. The
hydrogeochemistry and stream sediment reconnaissance
program (HSSR) was robust; lithogeochemistry and
groundwater were less extensive. The US Geological Sur-
vey eventually took responsibility of the NURE data and
sample repositories, retested 10% of the samples, and pro-
ceeded to put this Archival Data on the web (http://pubs.
usgs.gov/of/1997/ofr-97-0492/[.). In 2004, the USGS, in
collaboration with other agencies, conducted the national
geochemical survey (NGS) to produce a body of geo-
chemical data for the United States based primarily on
stream sediments, analyzed using a consistent set of meth-
ods. In 2004, the NGS included data covering 80% of the
land area of the US, including samples in all 50 states
(USGS 2004).
Several researchers have successfully used NURE data
(e.g. Bolivar 1980; Grossman 1998; Ried 1993; Cocker
1999). For example, Ried (1993) employed the (NURE)
stream sediment data to prepare a geochemical atlas of
North Carolina, USA. The North Carolina NURE database
consists of 6,744 stream sediment samples, 5,778 ground-
water samples, and 295 stream water analyses. Cocker
(1999) conducted geochemical mapping in Georgia, USA,
using NURE data as a tool for geological and environ-
mental studies, and mineral exploration. Results indicated
that bedrock geology and mineralization are the most
important variables which influence the stream sediment
and stream water geochemistry. Anthropogenic sources
influence the geochemistry to a lesser and more localized
extent. Ludington et al. (2006) studied the regional distri-
bution of arsenic and 20 other element in stream sediment
samples in northern Nevada and southeastern Oregon.
They used 10,261 samples from the NURE program in
their study area. They analyzed data using point maps and
provided interpolation between data points to construct
high- resolution raster images, which were correlated with
geographic and geologic information using a geographic
information system (GIS).
Statement of the problem
Large geochemical databases for regional geochemical
mapping (RGM) are very useful. Initially, one may think
of a map for every chemical, however, this is not an ade-
quate and need extensive data analysis. Such analyses
include detailed univarient analysis to identify normal and
Table 1 Examples of regional geochemical studies
References Location No. of
chemical
species
No. of
samples
Sample types Sample
density
(Km2)
The Wolfson Geochemical Atlas
of England and Wales
England and Wales 35 50,000 Stream sediment
NURE USA 397,609 Stream sediment 1/100
NURE USA 335,547 Water 1/100
Shacklette and Boerngen (1984) USA 40 1,300 Soil
Riemann and Filzmoser (2000) 10 countries around
the Baltic Sea
41 Soil 1/2,500
Xie and Ren (1993) China 39
Ried (1993) North Carolina 12,522 Stream sediment and
water
Cocker (1999) Georgia, USA 19 8,248 Stream sediment
The National Geochemical
Survey (NGS) (2004)
USA 1/289
Robinson et al. (2004) New England states 25 8,360 Stream sediment and
water
Ludington et al. (2006) Northern Nevada and
Southeastern Oregon
21 10,261 Stream sediment
1480 Environ Geol (2009) 58:1479–1497
123
anomalous behavior, and the normal group should be tested
for normality in both arithmetic and log transformed for-
mulations. When performing this on a large geochemical
database, several questions need to be addressed such as:
are the data lognormal? Beyond what concentration values
lie the outliers? What geochemical associations are present
in the data, and where are they located? Due to the fact that
earth’s surface is subdivided into drainage basins and dif-
ferent geologic materials, a typical regional geochemical
mapping study produce many possibilities and combination
of controlling factors to the geochemical behavior of ele-
ments, and the question becomes that of prioritizing the
combinations. Toward this end, spatial analyses by GIS
tools are important. With this in mind, the present study
deals with the aforementioned research elements and
questions for the entire State of New Mexico. An objective
is a comprehensive statistical and spatial analysis, with
interpretations, cautions, and directions ahead. This will
lead to a better understanding of the surface geochemistry
of New Mexico. Another objective is the development and
demonstration of a template to transfer product to the sci-
entific audience. Due to the large number of figures, and
tables the present paper will present figures for few
elements and the rest of elements and associated statistical
and spatial products of this RGM research are on the
data repository website at https://webspace.utexas.edu/
howarifm/www/NURE/1nm.htm/.
Study area
New Mexico is the fifth largest state in the USA, with a total
area of 121,412 square miles (314457.079 sq.km) and lies
between latitudes 32 and 37� and longitudes 103 and 109�W.
New Mexico is characterized by the following physiographic
provinces (Pazzaglia and Hawley 2004): (1) Great Plains in
the eastern third of the state, (2) San Juan Basin occupies all
of northwestern New Mexico, and this is the southeastern
portion of the much larger Colorado Plateau, (3) the Rio
Grande Rift runs south to north across the state, being more
narrow to the north, (4) Southern Rocky Mountains straddle
the Rift in the north, (5) Mogollon Volcanic Plateau, in west
central NM which is covered by massive ash flow tuffs, and
(6) the Basin and Range of southwestern NM. Figure 1
shows the general location of these provinces.
Stable Precambrian craton of North America underlies
the Great Plains. The same stable craton also underlies the
Colorado Plateau; however the Plateau has been elevated
several thousand feet. The present geology of NM is dom-
inated by the Rio Grande Rift, a major, young, extensional
event of North America. The cratonic crust has been domed
upward and infused with heat, and subsequent collapse has
produced the Rift Valley. The Southern Rocky Mountains
are a portion uplifted by Rift processes. Erosion eastward
from the Southern Rocky Mountains and the Rift zone in
general provided for the deposition of sediments onto the
Fig. 1 Digital shaded relief
map of New Mexico with
physiographic provinces
Environ Geol (2009) 58:1479–1497 1481
123
stable craton constituting the Great Plains as a large alluvial
fan. The Mogollon Volcanic Plateau is a silicic large
igneous province (SLIP) where widespread, short duration,
magmatism was associated with the RG Rift. Progressive
stretching of the crust and collapse by listric faulting also
produced the basins in southwestern NM, which became
filled with sediments, forming the Basin and Range prov-
ince. Young mafic volcanism is expressed as extensive
basalt flows. The detailed geology of NM is complex; for
more information refer to Mack and Giles 2004.
For the present study, a simplified geologic map is given
in Fig. 2. Drainage basin boundaries in New Mexico are
given in Fig. 3. Stream sediments are the medium in this
study. Actually, only a low percentage of the samples are
from streams with water. Most samples are sieved from
material in dry streams called arroyos. In mountainous
regions stream sediments are closer to their bedrock source,
and bedrock distribution may vary over short distances.
Stream sediment chemistry may have greater variance. On
the Great Plains or in the basins the stream sediments tend
toward homogenization. Natural chemical leaching also
takes place during sediment transport.
Dataset
The relevant, edited, database for New Mexico consists of
22 chemical elements. Sample locations are given in
Fig. 4. Data presentation and discussion here will be lim-
ited to two chemicals, Mg and Ce, for reasons of space.
These include one major and one trace element, and serve
to illustrate the manner of data presentation. Study of all
chemicals is given on the web site (https://webspace.
utexas.edu/howarifm/www/NURE/1nm.htm/). The full
stream sediment data and analytic methods for the national
uranium resource evaluation (NURE) program are avail-
able as U.S. Geological Survey Open-File Report
97–492.\http://pubs.usgs.gov/of/1997/ofr-97-0492/[. The
samples were taken during 1975–1979. Chemical analyses
were performed at both the Los Alamos Scientific Labo-
ratory (LASL) (now LANL, Los Alamos National
Laboratory) and at Oak Ridge (formerly ORGDP, now
ORNL, Oak Ridge National Laboratory). The NURE pro-
gram was terminated prematurely, no synthesis or
interpretation was accomplished, and the data and samples
were almost forgotten.
NURE data are organized into data sets that are arranged
by quadrangles of 1 9 2 degrees in area. Geochemical data
used in this study are from the Albuquerque, Aztec,
Brownfield, Carlsbad, Clifton, Clovis, Dalhart, Douglas,
Durango, El Paso, Fort Sumner, Gallup, Hobbs, La Junta,
Las Cruces, Raton, Roswell, Saint Johns, Santa Fe, Ship-
rock, Silver City, Socorro, Tucumcari, and Tularosa
quadrangles. The statewide database consists of 27,798
stream sediment sites. The location of stream sediment
samples from the NURE Program are shown in Fig. 4.
Fig. 2 Generalized geological
map of New Mexico
1482 Environ Geol (2009) 58:1479–1497
123
Average sample density in New Mexico is one per 11 km2.
Several areas have enhanced sample density, as seen on
this figure. They are (1) Estancia Valley Pilot Survey where
results from 2,992 sediment and 505 water samples are
published (LASL 1977); (2) Grants Special Study where
results from 3,569 sediment and 167 water samples were
Fig. 3 Drainage basin
boundaries of New Mexico
Fig. 4 Locations of stream
sediment samples from NURE
program (1975–1979) of New
Mexico
Environ Geol (2009) 58:1479–1497 1483
123
published (LASL 1981a); and (3) San Andres and Oscura
Mountains Detailed Survey where results from 884 stream
sediments were published (LASL 1981b).
Methodology
The methodology of this study is straightforward. The data
of interest were located and downloaded. Quadrangle data
from within state boundaries were selected. Data from
multiple quadrangles were merged into master state data-
base. The dataset were checked for missing values and
sample concentrations below detection limits. Then as
statistical analysis and spatial analysis were carried out.
The statistical analysis included univariate analysis,
bivariate analysis and multivariate analysis. Univarient
analysis includes identification of outliers in raw data.
Outliers were removed, and the remaining data are labeled
as no outlier (NO). Logrithmic transformations of raw and
NO data are made. Four sets of data are then subjected to
plotting of cumulative frequency, boxplots, and histograms
and a summary of observations were made. Data were
tested for normality as will be explained in subsequent
sections. Bivarient analysis consists of the generation of a
table of correlation coefficients and pairwise correlation
coefficients. Scattergrams of some data have been made.
Multivarient analysis consisted of dendograms and princi-
ple component or factor analysis, including factor score
coefficients for every sample.
The NURE data are joined to the point coverage created
by the GIS from latitude and longitude data for each
sampling point. Geochemical mapping of the data were
produced using ArcGIS. Maps showing the actual numer-
ical concentration of the elements at their locations are not
produced in regional geochemical mapping studies, rather,
points or sample locations are color-coded according to the
concentration ranges of the element, with the highest range
shown in hot colors, and the lowest ranges shown in cold
colors. The geochemical data for stream sediments then
were interpolated in grid format to provide a graphical
visualization of the regional variation in geochemical val-
ues. The method of spatial interpolation is the inverse
distance weighted (IDW). The IDW techniques were rec-
ommended in studies comparing interpolation methods
(Robinson et al. 2004). This method is applied with 12
neighboring samples used for estimation of each grid point.
The power of one has been chosen to acquire some degree
of smoothing effect. The color-scheme of these maps was
similar to that used in the point maps. These maps were
useful for defining regional trends and local anomalies and
providing a quick visual check of the data processing.
Spatial analysis produces many different types of maps.
In the present study, the maps were developed for each
chemical and are labeled by the chemical symbol and are
numbered, where (1) is a point map of raw data, (2) is IDW
grid map of the raw data, (3) is geologic polygon extraction
from the raw data and the average for each polygon is color
scaled, (4) is drainage basin polygon extraction from the raw
data and the average for each polygon is color scaled, (5) is a
point map of the outliers and their values. ArcMap� 9.1 was
used to display the final maps. Finally, factor score coeffi-
cients generated from multivarient statistics are plotted on
maps. The raw data are stored in a dBASE file (dbf format),
and basic calculations are performed using Microsoft
Excel�. Most of the statistical calculations are accomplished
with SPSS� software (version 11.5) and JMPIN� (version
3.2.6). The geochemical maps are produced with Arc GIS�
software (version 9.1), and Arc View� (version 3.3).
Detection and removal of outlier
Evaluation of anomalies requires cumulative frequency,
Normal quantile, outliers box, quantile box, and histogram
diagrams, and these are carried out by the JMP, SPSS (e.g.
Sall and Lehman 1996), and ArcMap. The outlier box
diagram (Fig. 5) displays the characteristics of the empir-
ical distribution for single elements at a glance: location,
spread, skewness, tail lengths and outliers (Tukey 1977).
Fig. 5 Definition of the outlier
box diagram
1484 Environ Geol (2009) 58:1479–1497
123
The Box represents 50% of ordered data stretching
between the lower hinge and the upper hinge, which rep-
resent the lower and the upper quartile of the data
respectively. The vertical bar in this box indicates the
median, which by its position depicts the symmetry or
skewness of the data. The outlier box diagram is used here
to define the threshold for anomalous values symbolized by
the upper fence (cutoff) which is found by adding a step
(1.5 of the h-spread) to the upper hinge. Another cutoff, the
lower fence is found by subtracting a step to the lower
hinge. The lower and upper whiskers extend to the two
most extreme data values that are still inside the fences.
This tool has been applied with success to regional geo-
chemical mapping (Reimann et al. 2004; Bounessah and
Atkin 2003; Kurzl 1988). This approach found to be better
than the mean ?2 or 3 standard deviation method, where
the mean is affected by outlier data. The outliers are then
extracted from the data, and the results are retested.
Test for normality
Normality and log normality is to be tested for each chemical
for both the raw data and the data without outliers. The
Kolmogorov–Smirnov (K–S) test measures the degree to
which a given data set follows aspecific theoretical distri-
bution (such as normal, uniform, or Poisson). The statistical
test of K–S is based on the largest absolute difference
between the observed and the theoretical cumulative dis-
tribution functions. The K–S test assumes that the
parameters (e.g., mean and standard deviation) of the test
distribution are specified in advance, whereas the Lilliefors
correction for the K–S test is applied when means and
variances are not known and must be estimated from the
data. Results were obtained for K–S test values performed
by JMPIN� statistical software which utilized the Lillierfos
correction automatically called the KSL test (Sall and
Lehman 1996). The KSL test is applied by JMPIN� if n
[2,000. If the p-value reported is less than 0.05 (or some
other alpha), then the distribution is not normal. Therefore it
is useful to use the normal quantile plot to help assess the
lack of normality in the distribution (Sall and Lehman 1996).
Data set classification
Outlier removal is based on outlier box diagrams. Data are
divided into Raw versus No-Outliers. Furthermore, it is of
interest to test both the numerical values and their logarithms,
leading to Log-Raw data and Log-No-Outlier data. The
division of the data set is into five classifications, which are:
(1) Raw data: original data with values below the
detection limit are replaced by half of the minimum
of the datasets.
(2) No-Outliers data: raw data with outliers removed.
(3) Log- Raw data: the base 10 logarithm of raw data set.
(4) Log- No-Outliers data: the base 10 logarithm of no-
outliers data set.
(5) Outliers data: The data that pass the whiskers where
the lower and upper whiskers extend to the two most
extreme data values that are still inside the fences of
the box diagram.
All diagrams are presented for every chemical in the
web site: https://webspace.utexas.edu/howarifm/www/
NURE/1nm.htm/ and examples are shown in Figs. 6, 7,
8, 9, 10, 11.
Multivarient analyses
In this study these analyses included cluster analysis (CA)
and principal component analysis (PCA). Cluster analysis
seeks to identify homogeneous subgroups of cases in a
population and identify a set of groups which both mini-
mize within-group variation and maximize between-group
variation. One of the general approaches to cluster analysis
is hierarchical clustering. The product of this approach is
dendrograms, also called tree diagrams, show the relative
size of the proximity coefficients at which cases were
combined. Whereas PCA is a technique to take linear
combinations of the original variables such that the first
principal component has maximum variation; the second
principal component has the next most variation subject to
being orthogonal to the first, and so on. Each principal
component is calculated by taking a linear combination of
an eigenvector of the correlation matrix with a standardized
original variable. The eigen values show the variance of
each component. The set of n principal components has the
same total variation and structure as the original variables.
In the present study principal component analysis is per-
formed to reduce a large number of variables to a smaller
number, and for further investigation of the relationships
between the elements. The principal components (PCs)
with eigenvalues larger than 1 are extracted with the PC
loadings rotated for the maximum variance. A total of five
PCs are extracted, which account for 73.53% of the total
variance as will be discussed in the subsequent sections.
Results
The present work examines several chemical elements,
which are Al, Ba, Ca, Ce, Co, Cr, Cu, Fe, K, La, Li, Mg,
Mn, Na, Ni, Pb, Sc, Th, Ti, U, V, and Zn. This list is
arrived at by editing; incomplete data sets are eliminated,
and the detection limit problem were addressed. Concen-
trations shown as below the detection limit (DL) are
Environ Geol (2009) 58:1479–1497 1485
123
replaced by half of the minimum detection limit of the
datasets for each element. Forty chemical constituents are
reported in the database. However, the data must be posted,
because the NURE Program was terminated prematurely,
and not all samples were analyzed for all constituents.
Initially editing has been accomplished as a result of the
process of cleaning and re-cleaning data from missing or
inconsistent parameters in the dataset. The original NURE
files were reformatted into two consistent database struc-
tures: one for water samples and a second for sediment
samples, on a quadrangle-byquadrangle basis, from the
original NURE files. In this study the reported elements
Fig. 6 Histograms and normal
quantile plots for No-Outliers
data set of Ce (the observed
values (in ppm or %) are plotted
on the x-axis, and values for a
normal distribution are plotted
on the y-axis)
1486 Environ Geol (2009) 58:1479–1497
123
were selected because of their availability throughout the
entire state of New Mexico, and quality of the data, which
are Al, Ba, Ca, Ce, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na,
Nb, Ni, Pb, Sc, Sr, Th, Ti, U, V, and Zn. Concentrations
shown as below detection limit are replaced by half of the
minimum of the datasets.
An inherited complication in the dataset is that the
analytical chemistry for some chemicals was carried out by
different laboratories. Sample backlogs at Los Alamos
resulted in Oak Ridge laboratories taking over samples
from specific areas for specific chemicals. Each laboratory
consistently produced slightly different numbers, for
Fig. 7 Histograms and normal
quantile plots for no-outliers
data set of Mg
Environ Geol (2009) 58:1479–1497 1487
123
Fig. 8 The dendrogram (cluster
tree) for the studied elements
Fig. 9 Point, IDW, geologic
and hydrologic polygons maps
for cerium in New Mexico
1488 Environ Geol (2009) 58:1479–1497
123
several chemicals. The results appear on maps, where
usually a 1 9 2 degree quadrangle boundary or a smaller
boundary appears as a line separating areas of slightly
different shading. When this problem is recognized, the
results of one laboratory could be calibrated to another data
set by a simple algorithm; and this is one of the limitations
of the dataset. A reason for rejecting some data is too many
samples being below the minimum detection limit.
Univarient analysis
The filtered data matrix for the studied chemical elements
is present in the data repository website of this paper
https://webspace.utexas.edu/howarifm/www/NURE/1nm.
htm/. These data are here subjected to several types of
statistical analyses as presented in Tables 2, and 3. How-
ever, univarient results for Mg and Ce are given in Figs. 6
and 7. The proceed data repository noted earlier shows that
elements such as Ba, Ce, Cu, Fe, La, Mn, Ni, Pb, Th, U, V,
and Zn have high concentration values. These high con-
centrations can be influenced by anthropogenic processes.
However, inadequate detection limits are observed for 13
elements: Ag, Au, Bi, Cd, Cl, Hf, Mo, Sb, Se, Sn, Ta, Tb,
and W. This is defined as the 50th percentiles for all these
elements are below detection limits. The element Be does
not have good precision (due to lack of analytical resolu-
tion along with only one significant figure). For example,
5–50th percentiles of Be are all equal to 1 ppm. These 14
elements are regarded as having inadequate data quality,
and are not used in the statistical analyses.
One the other hand, the 25th percentiles of concentra-
tions for all the other elements were above the detection
limits thus they are regarded as of adequate quality for this
study. This study will investigate 24 elements that cover
the entire state of New Mexico: Al, Ba, Ca, Ce, Co, Cr,
Cu, Fe, K, La, Li, Mg, Mn, Na, Nb, Ni, Pb, Sc, Sr, Th, Ti,
U, V, and Zn. Many elements have values below or close
to the detection limits (e.g., Co, Nb, Ni, Pb, Sr, Th, and
Zn) resulting in the high frequencies for the lowest value
group, near that limit of detection. The frequency distri-
butions of most of the elements (except for Al) are
positively skewed and include some very high values.
Some extreme values appear to be separated from the
majority of the samples, and do not appear to be part of a
continuous distribution. These extreme values might be
regarded as evidence of mineralization or anthropogenic
processes.
Bivariant analysis
Bivariate analyses in this study are correlation coefficients,
pairwise correlations, correlation scatter diagrams, and
correlation frequencies. These analyses are carried out for
the logarithmic transformation of the No-Outlier data set
because it is closer to the normality condition which is
required for correlation analysis. Correlation frequencies
Fig. 10 Point, IDW, geologic
and hydrologic polygons maps
for magnesium in New Mexico
Environ Geol (2009) 58:1479–1497 1489
123
depend on a coefficient value greater than 0.29, and is the
number of other elements with which they correlate above
this number. Ca, Pb and Th have frequencies less than 5, K,
Mg, Na, and U have frequencies between 5 and 9, and all
other elements have frequencies greater than 10. Bivarient
analyses are not presented here, but are at the data
Fig. 11 Spatial distribution of
cerium and magnesium outliers
in New Mexico
1490 Environ Geol (2009) 58:1479–1497
123
Ta
ble
2D
escr
ipti
ve
stat
isti
cso
fth
era
wd
ata
(Al,
Ca,
Fe,
K,
Mg
,N
aar
ein
wei
gh
tp
erce
nt;
all
oth
ers
are
inp
pm
)
Ele
men
tn
Min
5%
10
%2
5%
Med
ian
75
%9
0%
95
%M
axM
ean
SD
Sk
ewn
ess
Ku
rto
sis
Al
25
,03
50
.02
52
.83
.33
4.1
45
5.0
45
.99
6.8
67
.31
51
8.2
85
.06
1.3
90
.03
0.5
1
Ba
25
,03
52
31
33
64
44
35
41
66
28
02
.49
16
34
57
05
85
.98
45
2.7
13
6.6
02
14
7.6
7
Ca
25
,03
20
.02
50
.37
0.4
90
.79
1.5
93
.14
5.7
37
.83
29
.97
2.4
92
.66
2.6
71
0.4
9
Ce
25
,03
33
.52
73
34
45
87
29
01
09
10
00
61
.70
30
.99
5.0
17
6.0
3
Co
24
,95
30
.35
0.3
53
.45
79
.21
31
6.8
71
.97
.72
5.0
82
.58
14
.60
Cr
25
,03
10
.51
31
82
43
24
56
17
88
15
38
.19
27
.34
5.4
46
9.5
9
Cu
24
,89
71
71
01
41
92
53
33
91
42
26
21
.94
95
.10
13
4.9
92
00
05
.49
Fe
25
,03
40
.04
0.9
01
.13
1.5
52
.07
2.7
63
.79
4.8
74
9.8
22
.40
1.6
86
.22
97
.07
K2
5,0
35
0.0
10
.86
1.0
11
.24
61
.51
1.8
2.0
76
2.2
49
7.3
04
1.5
30
.44
0.5
73
.50
La
25
,03
21
12
15
21
27
34
43
52
46
72
9.2
21
6.4
06
.55
10
0.8
6
Li
21
,13
40
.51
31
51
92
43
13
94
62
24
26
.32
11
.78
2.9
42
1.3
6
Mg
25
,03
50
.02
50
.20
40
.29
0.4
20
.63
60
.94
1.3
31
.68
7.6
70
.75
0.5
02
.19
9.6
6
Mn
25
,03
52
16
42
03
28
13
94
55
67
57
.49
10
29
,83
04
55
.53
34
4.5
22
8.2
22
16
4.0
7
Na
25
,03
50
.02
0.3
31
0.4
30
.61
0.8
31
.11
1.4
61
.69
12
4.3
40
.90
0.4
21
.12
2.3
7
Nb
24
,89
72
22
26
11
16
21
26
27
.73
7.9
14
.82
75
.68
Ni
24
,89
71
11
11
11
72
43
17
42
11
.91
12
.75
10
.07
45
5.0
4
Pb
24
,89
72
.52
.52
.59
14
21
28
33
96
91
17
.96
92
.54
83
.53
79
48
.41
Sc
25
,03
40
.05
2.6
34
67
.61
01
1.4
48
.86
.21
2.9
41
.66
7.3
6
Sr
25
,03
43
33
31
19
22
03
78
49
76
,06
71
55
.62
22
0.8
04
.99
59
.34
Th
25
,03
30
.55
0.5
53
58
11
14
17
.73
32
.59
.00
8.5
81
0.3
02
29
.29
Ti
24
,40
49
.51
45
4.2
51
77
82
28
0.2
52
93
0.5
38
99
51
98
.56
63
5.5
47
,68
03
38
2.6
32
15
3.7
75
.03
51
.06
U2
7,3
51
0.1
1.7
76
22
.42
.93
.55
4.7
08
6.1
04
44
5.1
3.3
84
.06
56
.48
54
25
.28
V2
5,0
35
12
63
24
25
57
21
01
13
31
,51
26
4.6
64
7.3
76
.42
89
.35
Zn
24
,88
61
.51
.51
.53
35
37
41
01
12
41
2,6
68
58
.89
14
4.9
26
1.5
34
59
2.5
6
Environ Geol (2009) 58:1479–1497 1491
123
Ta
ble
3D
escr
ipti
ve
stat
isti
cso
fth
en
o-o
utl
iers
dat
a(A
l,C
a,F
e,K
,M
g,
Na
are
inw
eig
ht
per
cen
t;al
lo
ther
sar
ein
pp
m)
Ele
men
tn
Min
5%
10
%2
5%
Med
ian
75
%9
0%
95
%M
axM
ean
SD
Sk
ewn
ess
Ku
rto
sis
Al
24
,82
11
.38
2.8
53
.36
94
.15
75
.04
5.9
86
.84
7.2
78
.74
5.0
71
.33
-0
.01
-0
.25
Ba
23
,97
01
16
31
83
65
44
15
35
64
77
62
82
79
89
54
9.2
61
52
.77
0.3
1-
0.1
3
Ca
23
,00
70
.05
0.3
80
.49
0.7
61
.45
2.6
84
.25
.16
6.6
61
.92
1.4
91
.18
0.6
9
Ce
23
,95
33
.52
73
34
45
77
08
39
21
13
57
.42
19
.61
0.2
3-
0.0
4
Co
21
,79
50
.73
.74
57
91
1.1
13
15
.47
.26
2.8
10
.65
0.0
8
Cr
23
,47
41
14
18
24
32
43
54
61
76
33
.93
14
.16
0.5
9-
0.0
1
Cu
23
,29
72
91
01
41
82
43
03
44
11
9.4
17
.60
0.5
6-
0.1
0
Fe
23
,57
00
.08
0.8
91
.11
1.5
12
.01
2.5
97
3.2
83
.73
4.5
82
.11
0.8
40
.54
0.0
3
K2
4,6
60
0.4
20
.87
1.0
12
1.2
51
.51
1.7
92
.05
42
.20
62
.62
91
.52
0.4
00
.11
-0
.25
La
23
,72
52
13
16
21
27
33
40
44
53
27
.28
9.4
00
.27
-0
.15
Li
20
,33
02
13
15
19
24
30
37
41
48
24
.82
8.2
60
.53
0.0
1
Mg
23
,34
80
.05
0.2
40
.30
59
0.4
27
0.6
21
0.8
91
.17
81
.36
51
.71
90
.69
0.3
40
.76
0.0
6
Mn
24
,04
94
16
32
01
27
63
84
53
16
97
78
59
67
41
7.4
11
88
.47
0.6
6-
0.0
9
Na
24
,25
30
.04
20
.33
0.4
26
0.6
0.8
21
.08
1.3
71
.54
1.8
59
0.8
60
.36
0.4
7-
0.1
8
Nb
12
,75
54
67
81
01
31
72
02
41
1.2
24
.13
0.9
20
.57
Ni
16
,23
12
68
11
15
20
26
30
40
15
.85
7.2
50
.83
0.6
0
Pb
20
,95
65
68
11
15
21
27
30
38
16
.58
7.3
70
.58
-0
.28
Sc
24
,25
60
.12
.63
45
.87
.39
.11
0.3
12
.95
.91
2.3
60
.49
-0
.10
Sr
13
,88
26
82
.15
10
31
34
18
52
77
38
94
48
54
42
15
.79
11
0.8
30
.94
0.2
2
Th
22
,58
71
.13
46
8.1
10
.71
31
4.9
19
.98
.38
3.6
20
.38
-0
.10
Ti
22
,91
91
91
45
51
,76
42
,24
32
,85
43
,67
64
,57
35
,12
46
,32
63
01
5.5
71
11
1.2
20
.52
0.1
5
U2
5,2
67
0.6
81
.71
22
.38
2.8
3.3
64
4.4
5.2
72
.90
0.7
90
.48
0.2
5
V2
3,2
62
22
63
24
15
36
78
49
51
16
55
.41
20
.37
0.5
80
.25
Zn
19
,91
83
26
32
43
58
75
96
11
01
35
61
.22
24
.82
0.6
20
.10
1492 Environ Geol (2009) 58:1479–1497
123
repository website of this paper (https://webspace.utexas.
edu/howarifm/www/NURE/1nm.htm/).
Spatial analysis
For Spatial analysis (Figs. 8, 9, 10, 11), the NURE data are
joined to the point coverage created by the GIS from lati-
tude and longitude data for each sample location.
Geochemical mapping of the data are produced using
ArcGIS. Maps showing the actual numerical concentration
of the elements at their locations are not produced in
regional geochemical mapping studies, rather, points or
sample locations are color-coded according to the con-
centration ranges of the element, with the highest range
shown in hot colors, and the lowest ranges shown in cold
colors. The geochemical data for stream sediments then are
interpolated in grid format to provide a graphical visuali-
zation of the regional variation in geochemical values. The
method of spatial interpolation is the inverse distance
weighted (IDW). The IDW techniques are recommended in
studies comparing interpolation methods (Robinson et al.
2004), and it available in the software. This method is
applied with 12 neighboring samples used for estimation of
each grid point. The power of one is chosen to acquire
some degree of smoothing effect. The color-scheme of
these maps is similar to that used in the point maps. These
maps are useful for defining regional trends, local anom-
alies, background, and providing a quick visual check of
the data processing.
Maps are also produced by color-coding polygons in a
pre-existing map. These maps are a geologic map, and a
drainage basin map base. Colors are assigned according
to the median concentration of the element in samples
falling within each polygon (Grossman 1998). Median
values are generally the best measure of central tendency
in regional geochemical datasets (Reimann and Filzmoser
2000). Median values of the chemical data are calculated
for map polygon areas using the Point-Stat-Calc exten-
sion for ArcView v.3.3 (Dombroski 2000). The legends
for the median value maps are based on the standard
deviation of the range in median values of data for
each of the polygons of grouped stream sediment sample
sites.
Spatial analysis here consists of several maps for each
chemical, which are (1) Point map of raw data; (2) IDW
grid map of the raw data; (3) Extraction of map 1 data for
each geologic polygon (Fig. 2) calculating descriptive
statistics for each polygon, and scaling the results; (4)
Extraction of map 1 data for each drainage basin (Fig. 3)
polygon, and scaling the results; (5) Scaled point map of
the outlier values. Spatial analyses maps for the examined
elements are presented at: https://webspace.utexas.edu/
howarifm/www/NURE/1nm.htm/ as well as Figs. 9, 10, 11.
Spatial distribution of factor scores
The five principal components of the data set from Table 4
are used in this section to show spatial distribution of the
factor scores. The factor scores for each sample are cal-
culated by summing the product of the loading or weights
shown in Table 5 multiplied by the corresponding geo-
chemical values. The factor scores for each sample are
plotted on maps at the data repository website of this paper,
and examples are being presented in the discussion section
of the present paper.
Median value rock chemistry for New Mexico
The simplified geologic map, Fig. 2, is layered over a map
of the chemistry of every element. The median value of
chemical concentration of all samples falling within a
polygon of a geologic unit is determined, and those values
are presented as the chemistry of that geologic unit.
Inherent questions are present, such as using the chemistry
of sediment to be equivalent to that of nearby bedrock.
Figure 12 consequently is for Mg, and Ce, and it shows the
median and range in concentrations of Mg and Ce in New
Table 4 Principal component loadings from PCA after rotation for
the maximum variance
PC1 PC2 PC3 PC4 PC5
Al 0.54 0.47 0.20 0.45 0.24
Ba 0.16 0.76 0.13 -0.04 0.13
Ca 0.17 -0.18 -0.70 0.04 -0.01
Ce 0.49 0.32 0.63 0.23 0.19
Co 0.86 0.22 0.12 0.05 0.16
Cr 0.87 -0.06 0.08 0.09 -0.14
Cu 0.69 0.10 0.00 0.36 0.18
Fe 0.84 0.36 0.06 -0.03 0.11
K -0.06 0.48 0.14 0.75 -0.07
La 0.44 0.32 0.68 0.24 0.15
Li 0.47 -0.04 -0.07 0.73 0.00
Mg 0.52 -0.13 -0.54 0.35 0.01
Mn 0.59 0.45 0.16 0.17 0.18
Na 0.07 0.82 0.11 0.18 -0.04
Ni 0.81 -0.08 -0.07 0.25 0.00
Pb 0.07 0.09 0.12 -0.01 0.93
Sc 0.83 0.06 0.24 0.22 0.06
Th 0.14 -0.19 0.46 0.30 0.27
Ti 0.62 0.53 0.28 -0.02 -0.17
U 0.26 0.07 0.73 -0.04 -0.01
V 0.84 0.26 0.07 -0.19 0.00
Zn 0.70 0.09 0.14 0.36 0.14
% of variance 33.25 12.58 12.38 9.64 5.68
Cumulative % 33.25 45.83 58.21 67.84 73.53
Environ Geol (2009) 58:1479–1497 1493
123
Mexico rock types. This diagram is presented for every
chemical element, and includes all the simplified rock
types. The Geologic Rock Units are derived from a mod-
ified Digital Generalized Geologic Map of United States,
Puerto Rico, and the US Virgin Islands, in ARC/INFO
Format (Reed and Bush 2005). The associated geologic
rock units categories are listed on the web: https://
webspace.utexas.edu/howarifm/www/NURE/1nm.htm/.
Discussion
Interpretation of the distribution of the chemical data has
mainly based on mineralogy, lithological units and geol-
ogy. The presented results indicate that the elements
reported here are present largely in minerals. However,
elements bounded to other geochemical phase of sediments
e.g. absorbed or colloidally bound chemicals may also be
present; adsorbed would increase with the iron content,
although this is probably minor in occurrence but believed
to exist. For example the outliers of Ce are concentrated in
the felsic igneous rock centers of the El Capitan area, and
the Organ Mountains. Outliers with moderate values are in
the Mogollon Volcanic Plateau. Another significant area of
outliers is in the Burro Mountains northeast of Lordsburg
which more prominent than the area of outliers is in the
Boot Heel of New Mexico. In the San Juan basin are many
outliers which are not easily explained. Young volcanic
rock of north central New Mexico also contains numerous
outliers. Whereas Mg is associated with Ca in carbonate
rocks and also it is rich in mafic igneous rocks. In southeast
New Mexico, Mg is associated with sedimentary rocks.
The dramatic number of outliers immediately north of
latitude 34� is influenced by the analytical methodology.
The outliers in the northwest corner of New Mexico are
associated with carbonate rocks of the San Juan basin.
Mineralogy consists of major minerals, quartz and clay,
minor minerals such as Fe or Mn oxyhydroxides, and
accessory, trace, or resistate minerals, such as magnetite,
zircon, rutile, and others. Multielement cluster analysis
separates the 22 chemical into 5 important groups (Fig. 8).
Although all the noted locations have outliers but they do
differ from one another mainly due to geological and
mineralogical conditions.
1. (Ce, La, Al, and Mn). This is a rare earth element
signature of peraluminous granites.
The REEs are found in resistate minerals in the sedi-
ment, such as monazite, zircon, and others.
2. (Fe, V, and Ti). Magnetite component present as a
resistate in the sediment.
3. (Co, Sc, Cr, Ni, Cu, and Zn). These chemicals are a
combined mafic geochemical indicator.
4. (K, and Li), (U, Th, and Pb). This is an alkali metal and
actinide signature of alkali granites.
5. (Na and Ba)
Fig. 12 The median and range concentrations of Mg and Ce in New
Mexico rock types
Table 5 Groups of elements based on the principal components
loading
5 factors incorporate of the 73.53% variance
Eigen
value
Major
elements
Trace elements Negative
F1 = 7.32 Al, Fe Co, Cr, Cu, Mg, Mn,
Ni, Sc, Ti, V, Zn
F2 = 2.77 Na Ba, Ti
F3 = 2.72 Ce, La, U Ca, Mg
F4 = 2.12 K Li
F5 = 1.25 Pb
1494 Environ Geol (2009) 58:1479–1497
123
6. (Ca and Mg). This is a carbonate signature. These two
elements belong to the same family in the column IIA
in the periodic table, and all are enriched in limestone.
They are mobile in the environment, and in sediments
they share similar behaviors of enrichment and
depletion.
Based on the Principal Component Analysis, the 22
elements can be grouped into five PCs. It was expected to
trace the elements back and forth between the cluster
analyses and PCA to large extent, but the results does not
support this; which indicate that not all the element control
the distribution or geochemical association in the studied
sediment in the same manner. The element groups pre-
sented in the PCA accounts for the major variances of
dataset as will be described next. The element classification
from PCA is reflecting part of the results from cluster
analysis. Table 5 shows the five groups of elements based
on PC loading.
1. Factor (1) (Al, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sc, Ti, V,
and Zn)
2. Factor (2) (Ba, Na, and Ti)
3. Factor (3) (Ce, La and U), (Ca and Mg)
4. Factor (4) (K and Li)
5. Factor (5) (Pb)
Factor (1) accounts for 33.25% of the total variance,
and contains a high loading of Al, Co, Cr, Cu, Fe,
Mg, Mn, Ni, Sc, Ti, V, and Zn. This group can be
further subdivided into subfamilies, such as the
mafic trace elements with the presence of Co, Cr,
Fe, Sc, Ni, and V, ultramafic rocks with the presence
of Cr, Co, Ni, and Cu, (Levinson 1980; Thornton
1983).
Factor (2) accounts for 12.58% of the total variance,
and contains a high loading of Ba, Na, and Ti. These
elements have high concentrations in felsic igneous
rocks.
Factor (3) accounts for 12.38% of the total variance,
and contains a high loading of (Ce, La and U), and a
negative loading of (Ca and Mg). This group
suggests the rare earth element granite, are a family
in resistate minerals.
Factor (4) accounts for 9.64% of the total variance,
and contains a high loading of K and Li. These two
elements are the alkali metals characteristic of alkali
igneous rocks.
Factor (5) accounts for 5.68% of the total variance,
and contains a high loading of Pb with a value of
0.93. The fact that this lead component is singular,
and that it is not associated with Cu or Zn, denies the
possibility that it is associated with mineral deposits.
It is probably of anthropogenic origin, specifically
leaded gasoline. All gasoline contained lead in the
late 1970s. Factor 5 needs to be investigated more.
The five principal components of the data set are used in
this section to show spatial distribution of the factor scores.
The factor scores for each sample is calculated by summing
the product of the loading or weights shown in Table 3
multiplied by the corresponding geochemical values. The
first factor is dominated by Al, Co, Cr, Cu, Fe, Mg, Mn, Ni,
Sc, Ti, V, and Zn. Figure 13a shows high scores are closely
associated with the widespread, young, mafic igneous
rocks, with some variability, and indeed, this factor is a
mafic association of elements. The map shows very clear
and interesting regional structures and sharp boundaries
emerge. The most unusual feature is a clear liner zone
extending from the Raton volcanic field in the northeast
towards the Mogollon Volcanic Plateau, marking a linea-
ment which is known as the Jemez lineament. The Jemez
lineament is a northeast-trending zone characterized by
alignment of late Miocene through Quaternary bimodal
volcanic rocks (Pazzaglia and Hawley 2004). The third
factor is dominated by two groups which are Ce, La and U,
and negatively by Ca and Mg. Figure 13b shows the high
positive score with widespread felsic volcanic rock and no
limestone. This factor is dominated by the rare earth ele-
ments (REEs).
Future work
It has been said that research creates more questions than it
answers. The present research is a strong example of this,
and next are some additional questions to be considered by
the data.
1. Chemistry of the Great Plains. Sediments of the Great
Plains were deposited as a large alluvial fan east of the
rising Southern Rocky mountains. Later, the Pecos
River incised into this fan, and a divide is present east
of the Pecos, such that a cross section of the alluvial
fan is present to the west of that divide. Is there
variable chemistry in the fan?
2. From the above, can mineralogy be built from the
chemistry, because the mineralogy of the Great Plains
is relatively simple? Can remote sensing band combi-
nation maps which respond to mineralogy be merged
or layered with regional geochemical mapping?
3. Chemical footprint of large copper smelters. Two large
copper smelters are present in Southern New Mexico,
one over 100 years old, and the other young. Can those
footprint be revealed?
4. Special study area in the Grants Uranium District.
Higher sample density and better arsenic and uranium
analytical methodology is an opportunity for study.
Environ Geol (2009) 58:1479–1497 1495
123
Fig. 13 Spatial distribution of
high factor scores (the factor
scores for each sample are
calculated by summing the
product of the loading or
weights multiplied by the
corresponding geochemical
values; then factor scores for
each sample are plotted on
maps)
1496 Environ Geol (2009) 58:1479–1497
123
5. Correlation of geologic distribution of (young) mafics
and Mafic Factor.
6. Subsets having homogeneity of source rock and rock,
sediment, water chemistry provide unique geochemical
partition coefficients between sample types. One
example at Capitan has been accomplished. The rest
need to be investigated.
Conclusions
The interpretations of the regional geochemical mapping of
New Mexico from NURE datasets made possible by the
generation of various figures, maps, and tables. The study
found that PCA and GIS mapping of NURE stream sedi-
ment data are powerful tools in defining the regional and
local geochemical patterns related to the underlying geol-
ogy and anthropogenic sources. The study reported
geochemical anomalies that indicate several mineral belts
in New Mexico. These anomalies are clusters in several
areas including the Mogollon Volcanic Plateau, the Valles
caldera, the San Juan basin and the El Capitan Mountains,
as well as the main mining districts, and the major mining
industries present in the area. PCA shows a clear trend with
the association of the elements Co, Cr, Fe, Ni, Sc, and V.
This association is described as indicative of a mafic
chemistry signature. The mafic factor clusters in the Rio
Grande rift and Jemez lineament. On the other hand, the
REE factor consists of Ce, La and U, and it has strong,
localized clusters in the Organ Mountains, Boot Heel, San
Andres Mountains and El Capitan Mountains. The study
also concluded that the common REE elements are found
in certain felsic igneous rocks and in pegmatites. Mainly
the distribution of the elements in stream sediments in New
Mexico shows that most of the variability is controlled by
the bed rock chemistry.
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